Conventionally, an X-ray CT apparatus which irradiates a subject with X-rays, detects X-rays transmitted through or scattered by the subject using an X-ray detector, and makes a fluoroscopic image, tomosynthesis, or three-dimensional (3D) image on the basis of this X-ray detection output (the number of photons of X-rays) is known.
As such X-ray CT apparatus, a cone beam CT apparatus has been developed. In a normal X-ray CT apparatus, an X-ray beam is limited to be thin in the Z-direction, and is called a fan beam. However, cone beam CT (CBCT) uses an X-ray beam which also spreads in the Z-direction, and is called a cone beam.
In multi-slice CT or CBCT, images are generated in large quantities as imaging results. Hence, a diagnosis aiding device for a doctor, which selects and presents images suited for diagnosis from the images in large quantities, becomes important. Especially, detecting a nodule from a chest CT image from which a pathology is relatively easily detected meets a requirement upon using CT in the medical examination purpose. Conventionally, many researchers have made studies about detection of a nodule from an axial image.
For example, a specific region (nodule) used to determine whether or not it is a tumor is extracted from two-dimensional (2D) image (axial) information, an area S of that region is calculated, and the centroid of the extracted region is calculated. A radius r of a circle having the same area as the extracted region (to be referred to as an equivalent radius of the extracted region hereinafter) is calculated by:r=(S/π)½  (1)
Next, the circularity of the extracted region is calculated. The circularity is defined using the calculated centroid as the center on the basis of the ratio of the area of the extracted region included in the circle of the calculated equivalent radius r by:Circularity=area of extracted region included in circle/S  (2)
In a feature space determined by the equivalent radius r and circularity, if the equivalent radius r and circularity are included in a given range, it is determined that the extracted region is a tumor.
Japanese Patent Laid-Open No. 07-236634 has proposed the following apparatus. That is, regions are extracted from a large number of pieces of 2D image information which are distributed three-dimensionally in place of only one image, and the equivalent radii and circularities of the extracted regions are calculated. Then, tumors are detected for respective images on the basis of the circularities.
Japanese Patent Laid-Open No. 08-066392 has proposed a diagnosis aiding system comprising:
a first processing function of obtaining an image of interest by extracting density data values of CT images obtained by reconstruction using a desired first threshold value with a low density data value;
a first determination function of determining based on a feature amount such as a shape or the like of the extracted image of interest if the image of interest is a nodule;
a second processing function of obtaining an image of interest by extracting density data values of CT images using a desired second threshold value with a high density data value;
a candidate determination function of determining based on a feature amount such as a shape or the like of the extracted image of interest if the image of interest can be a candidate of a nodule;
a third processing function of acquiring an enlarged CT image of a predetermined scale by reconstructing the image of interest determined as the candidate by zooming from projection data, and extracting the enlarged CT image using the first threshold value to obtain an image of interest; and
a second determination function of determining based on a feature amount such as a shape or the like of the image of interest extracted in the third processing function if the image of interest is a nodule.
Japanese Patent Laid-Open No. 08-166995 has proposed a medical diagnosis aiding system which calculates a plurality of CT images from projection data obtained by projecting a 3D region of a patient, and provides information used to aid diagnosis of the patient on the basis of the plurality of CT images via an output device. This medical diagnosis aiding system comprises a CT image analysis means for determining a CT image suspected to be an affected area from the plurality of CT images as an image of interest, and a display control means for controlling the output device to display a plurality of first 2D images of a 3D region corresponding to the image of interest, which are emphasized from a plurality of second 2D images of the remaining 3D region.
Japanese Patent Laid-Open No. 2001-137230 has proposed a computer-aided diagnosis system comprising:
a region extraction unit for extracting a region of a specific organ from multi-slice first CT images and multi-slice second CT images which have a different imaging timing from the first CT images in association with the same portion of the same patient as the first CT images; and
a slice matching unit for matching an anatomical position associated with the body axis direction between the first and second CT images on the basis of indices associated with the sizes and shapes of the regions of the specific organ, which are extracted from the first and second CT images.
Japanese Patent Laid-Open No. 2002-078706 has proposed a computer-aided diagnosis method for aiding diagnosis using 3D digital image data.
This computer-aided diagnosis method comprises:
a step of identifying a 3D object in 3D digital image data;
a step of calculating a local rotation plane for one predetermined 3D object, the local rotation plane being centered about the centroid of the predetermined object and a local rotation axis;
a step of rotating the local rotation plane through at least a partial angle of 360°; and
a step of obtaining a plurality of observation images of the predetermined object by generating observation images of the predetermined object in prescribed increments of rotation.
All the aforementioned references have proposed techniques in which the image to be processed is based on an axial image. Conventionally, primary detection for detecting a nodule having a nearly circular shape is done on the basis of an axial image, and the nodule is determined based on the connectivity of candidates in other neighboring axial images.
As diagnosis aiding techniques using coronal images other than axial images, for example, a diagnosis aiding apparatus that utilizes a plurality of axial images has been proposed as Japanese Patent Laid-Open No. 7-265300. This diagnosis aiding apparatus comprises a means for extracting a region of interest from at least one of the plurality of axial images, and means for generating a plurality of coronal images by applying multi-planar reconstruction to images in the region of interest extracted by the extraction means of the plurality of axial images, and is wherein the plurality of axial images are used in diagnosis.
According to Japanese Patent Laid-Open No. 7-265300, coronal images are calculated by applying multi-planar reconstruction to only the region of interest of the axial images, and diagnosis is made using the coronal images. Hence, the number of images to be interpreted by the doctor can be reduced compared to axial images to be interpreted.
The reason why CAD processes are conventionally done based on axial images is that continuity in the body axis direction is not guaranteed before spiral CT. For this reason, artifacts are generated due to connection errors in the body axis direction if multi-planar reconstruction is made.
In Multi detector row spiral CT in recent years, roughly isotropic images can be constructed but are not perfect since spiral scan is done. By contrast, in CBCT that scans organs such as lungs by one revolution, since perfect isotropy is guaranteed, perfect coronal images can be generated.
On the other hand, since vein branches in lungs normally run in the up-and-down direction, a roughly circular nodule detected from an axial image is readily mistaken for a vein, resulting in no advantages.